CREME – python library for online ML
All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data.
The model learns from one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning might be what you're looking for.
Here are some benefits of using creme (and online machine learning in general):
• incremental – models can update themselves in real-time
• adaptive – models can adapt to concept drift
• production-ready – working with data streams makes it simple to replicate production scenarios during model development
• efficient – models don't have to be retrained and require little compute power, which lowers their carbon footprint
api reference: https://creme-ml.github.io/content/api.html
github: https://github.com/creme-ml/creme
#ml #online #learning
All the tools in the library can be updated with a single observation at a time, and can therefore be used to learn from streaming data.
The model learns from one observation at a time, and can therefore be updated on the fly. This allows to learn from massive datasets that don't fit in main memory. Online machine learning also integrates nicely in cases where new data is constantly arriving. It shines in many use cases, such as time series forecasting, spam filtering, recommender systems, CTR prediction, and IoT applications. If you're bored with retraining models and want to instead build dynamic models, then online machine learning might be what you're looking for.
Here are some benefits of using creme (and online machine learning in general):
• incremental – models can update themselves in real-time
• adaptive – models can adapt to concept drift
• production-ready – working with data streams makes it simple to replicate production scenarios during model development
• efficient – models don't have to be retrained and require little compute power, which lowers their carbon footprint
api reference: https://creme-ml.github.io/content/api.html
github: https://github.com/creme-ml/creme
#ml #online #learning
Practical ML Conf - The biggest offline ML conference of the year in Moscow.
- https://pmlconf.yandex.ru
- September 7, Moscow
- For speakers: offline
- For participants: offline and online (youtube)
- The conference language is Russian.
Call for propose is open https://pmlconf.yandex.ru/call_for_papers
#conference #nlp #cv #genAI #recsys #mlops #ecomm #hardware #research #offline #online
- https://pmlconf.yandex.ru
- September 7, Moscow
- For speakers: offline
- For participants: offline and online (youtube)
- The conference language is Russian.
Call for propose is open https://pmlconf.yandex.ru/call_for_papers
#conference #nlp #cv #genAI #recsys #mlops #ecomm #hardware #research #offline #online
Practical ML 2024 (PML) конференция для экспертов — использование ИИ для бизнеса | ML-конференция 2024 от Яндекса
Practical ML конференция для экспертов по внедрению ИИ в бизнес. Информационные доклады от ключевых разработчиков по работе с ML. PML Conf 2024 от компании Яндекс.